Python NaProblem示例

示例#1

from deephyper.benchmark import NaProblem
from candlepb.Uno.structs.uno_mlp_1 import create_structure
from candlepb.Uno.uno_baseline_keras2 import load_data_multi_array

Problem = NaProblem()

Problem.load_data(load_data_multi_array)

# Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_structure, num_cells=3)

Problem.hyperparameters(
    batch_size=64,
    learning_rate=0.001,
    optimizer='adam',
    num_epochs=1,
)

Problem.loss('mse')

Problem.metrics(['r2'])

Problem.objective('val_r2__last')

Problem.post_training(num_epochs=1000,
                      metrics=['r2'],
                      model_checkpoint={
                          'monitor': 'val_r2',
                          'mode': 'max',
                          'save_best_only': True,

示例#2

from deephyper.benchmark import NaProblem
from deephyper.benchmark.nas.linearRegMultiLoss.load_data import load_data
from deephyper.search.nas.model.baseline.simple_bi_model import create_search_space

from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem(seed=2019)

Problem.load_data(load_data)

# Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space, num_layers=10)

Problem.hyperparameters(batch_size=100,
                        learning_rate=0.1,
                        optimizer="adam",
                        num_epochs=20)

Problem.loss(
    loss={
        "output_0": "mse",
        "output_1": "mse"
    },
    weights={
        "output_0": 0.0,
        "output_1": 1.0
    },
)

Problem.metrics({"output_0": ["r2", "mse"], "output_1": "mse"})

示例#3

文件： problem.py 项目： stjordanis/deephyper

from deephyper.benchmark import NaProblem
from deephyper.benchmark.nas.linearReg.load_data import load_data
from deephyper.benchmark.nas.linearRegMultiInputsGen.load_data import load_data
from deephyper.search.nas.model.baseline.simple import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem()

Problem.load_data(load_data)

Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space)

Problem.hyperparameters(
    batch_size=100,
    learning_rate=0.1,
    optimizer='adam',
    num_epochs=10,
)

Problem.loss('mse')

Problem.metrics(['r2'])

Problem.objective('val_r2')

# Just to print your problem, to test its definition and imports in the current python environment.
if __name__ == '__main__':
    print(Problem)

示例#4

文件： problem.py 项目： Deathn0t/NAS-CD

from deephyper.benchmark import NaProblem
from nascd.ImprovedFishes.load_data import load_data
from nascd.ImprovedFishes.search_space import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem(seed=2019)

Problem.load_data(load_data)

Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space)

Problem.hyperparameters(
    batch_size=8,
    learning_rate=0.01,
    optimizer='adam',
    num_epochs=200,
    callbacks=dict(EarlyStopping=dict(
        monitor='r2',  # or 'val_acc' ?
        mode='max',
        verbose=0,
        patience=5)))

Problem.loss('mse')  # or 'categorical_crossentropy' ?

Problem.metrics(['r2'])  # or 'acc' ?

Problem.objective('r2__max')  # or 'val_acc__last' ?

Problem.post_training(

示例#5

文件： problem_exp1.py 项目： bigwater/candlepb

from deephyper.benchmark import NaProblem
from candlepb.Uno.structs.uno_mlp_1 import create_structure
from candlepb.Uno.uno_baseline_keras2 import load_data_multi_array

Problem = NaProblem()

Problem.load_data(load_data_multi_array)

# Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_structure, num_cells=3)

Problem.hyperparameters(
    batch_size=64,
    learning_rate=0.001,
    optimizer='adam',
    num_epochs=1,
)

Problem.loss('mse')

Problem.metrics(['r2'])

Problem.objective('val_r2__last')

# Problem.post_training(
#     num_epochs=1000,
#     metrics=['r2'],
#     # model_checkpoint={
#     #     'monitor': 'val_r2',
#     #     'mode': 'max',

示例#6

from deephyper.benchmark import NaProblem
from deephyper.benchmark.nas.mnist1D.load_data import load_data
from deephyper.search.nas.model.baseline.simple import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem()

Problem.load_data(load_data)

Problem.search_space(create_search_space)

Problem.hyperparameters(
    batch_size=100,
    learning_rate=0.1,
    optimizer='adam',
    num_epochs=10,
)

Problem.loss('categorical_crossentropy')

Problem.metrics(['acc'])

Problem.objective('val_acc')

# Just to print your problem, to test its definition and imports in the current python environment.
if __name__ == '__main__':
    print(Problem)

示例#7

from deephyper.benchmark import NaProblem
from deephyper.benchmark.nas.linearRegMultiVar.load_data import load_data
from deephyper.search.nas.model.baseline.simple_deep import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem(seed=2019)

Problem.load_data(load_data)

# Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space)

Problem.hyperparameters(batch_size=100,
                        learning_rate=0.1,
                        optimizer="adam",
                        num_epochs=1)

Problem.loss("mse")

Problem.metrics(["r2"])

Problem.objective("val_r2")

# Just to print your problem, to test its definition and imports in the current python environment.
if __name__ == "__main__":
    print(Problem)

示例#8

文件： problem.py 项目： YulianaGomez/power

## /Users/yzamora/nas_problems/nas_problems/polynome2
import time
from deephyper.benchmark import NaProblem
from deephyper.search.nas.model.preprocessing import minmaxstdscaler
from nas_problems.polynome2.load_data import load_data
from nas_problems.polynome2.architecture import create_search_space

Problem = NaProblem(seed=2019)

Problem.load_data(load_data)

#Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space, num_layers=10)

Problem.hyperparameters(
    verbose=0,
    batch_size=100,
    learning_rate=0.001,  #lr search: 0.01, lr post: 0.001
    optimizer='adam',
    num_epochs=50,
    callbacks=dict(EarlyStopping=dict(
        monitor='val_r2', mode='max', verbose=0, patience=5)))

Problem.loss('mse')

Problem.metrics(['r2'])

Problem.objective('val_r2__last')

Problem.post_training(num_epochs=1000,

示例#9

from candlepb.NT3.models.candle_conv_mlp_baseline import create_structure
from candlepb.NT3.nt3_baseline_keras2 import load_data
from deephyper.benchmark import NaProblem

Problem = NaProblem()

Problem.load_data(load_data)

# Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_structure)

Problem.hyperparameters(
    batch_size=20,
    learning_rate=0.01,
    optimizer='adam',
    num_epochs=1,
)

Problem.loss('categorical_crossentropy')

Problem.metrics(['acc'])

Problem.objective('val_acc__last')

Problem.post_training(num_epochs=1000,
                      metrics=['acc'],
                      model_checkpoint={
                          'monitor': 'val_acc',
                          'mode': 'max',
                          'save_best_only': True,

示例#10

from nas_problems.polynome2.load_data import load_data
from nas_problems.polynome2.search_space import create_search_space
from deephyper.benchmark import NaProblem

Problem = NaProblem()

Problem.load_data(load_data, size=1000)

Problem.search_space(create_search_space)

Problem.hyperparameters(
    batch_size=128,
    learning_rate=0.001,
    optimizer='rmsprop',
    num_epochs=5,
)

Problem.loss('mse')

Problem.metrics(['r2'])

Problem.objective('val_r2__last')

Problem.post_training(num_epochs=60,
                      metrics=['r2'],
                      model_checkpoint={
                          'monitor': 'val_r2',
                          'mode': 'max',
                          'save_best_only': True,
                          'verbose': 1
                      },

示例#11

文件： problem.py 项目： rmjcs2020/Geophysical_NAS

from deephyper.benchmark import NaProblem
import os
import sys
HERE = os.path.dirname(os.path.abspath(
    __file__))  # useful to locate data files with respect to this file
sys.path.insert(0, HERE)

from load_data import load_data
from search_space import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem(seed=2019)

Problem.load_data(load_data)

# Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space, num_layers=5)

Problem.hyperparameters(
    batch_size=32,
    learning_rate=0.001,
    optimizer='adam',
    num_epochs=20,
    callbacks=dict(EarlyStopping=dict(
        monitor='r2',  # or 'val_acc' ?
        mode='max',
        verbose=0,
        patience=10)))

Problem.loss('mse')  # or 'categorical_crossentropy' ?

示例#12

文件： problem_exp1.py 项目： bigwater/candlepb

from candlepb.NT3.models.candle_conv_mlp_1 import create_structure
from deephyper.benchmark import NaProblem

from candlepb.NT3.problems.load_data import load_data

import os
import numpy as np
from typing import Tuple
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler
from keras.utils import np_utils

Problem = NaProblem()

Problem.load_data(load_data)

Problem.search_space(create_structure)

Problem.hyperparameters(batch_size=20,
                        learning_rate=0.01,
                        optimizer='adam',
                        num_epochs=1,
                        ranks_per_node=1)

Problem.loss('categorical_crossentropy')

Problem.metrics(['acc'])

Problem.objective('val_acc__last')

# Problem.post_training(

示例#13

from deephyper.benchmark import NaProblem
from nascd.xorandor.load_data import load_data
from nascd.xorandor.search_space import create_search_space

# from deephyper.search.nas.model.preprocessing import stdscaler

Problem = NaProblem(seed=4968214)

Problem.load_data(load_data)

# Problem.preprocessing(stdscaler)

Problem.search_space(create_search_space)

Problem.hyperparameters(
    batch_size=2,
    learning_rate=1.0,
    optimizer="rmsprop",
    num_epochs=2500,
    verbose=0,
    callbacks=dict(EarlyStopping=dict(
        monitor="loss",
        mode="min",
        verbose=0,
        patience=5  # or 'val_acc' ?
    )),
)

Problem.loss("binary_crossentropy")  # or 'categorical_crossentropy' ?

Problem.metrics(["binary_accuracy"])  # or 'acc' ?

示例#14

文件： problem.py 项目： Deathn0t/NAS-CD

from deephyper.benchmark import NaProblem
from nascd.fishes.load_data import load_data
from nascd.fishes.search_space import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler

Problem = NaProblem(seed=2019)

Problem.load_data(load_data)

Problem.preprocessing(minmaxstdscaler)

Problem.search_space(create_search_space)

Problem.hyperparameters(
    batch_size=8,
    learning_rate=0.01,
    optimizer="adam",
    num_epochs=200,
    verbose=0,
    callbacks=dict(EarlyStopping=dict(
        monitor="r2",
        mode="max",
        verbose=0,
        patience=5  # or 'val_acc' ?
    )),
)

Problem.loss("mse")  # or 'categorical_crossentropy' ?

Problem.metrics(["r2"])  # or 'acc' ?